What?

Reanalysis of data in Appel et al (2015)’s The influence of stereotype threat on immigrants: review and meta-analysis.

Start

library(pacman)
p_load(kirkegaard, metafor)

Data

d = readxl::read_xlsx("data/Appel_studies.xlsx")

Meta-analyze

#rma
(main = metafor::rma(d$d, sei = d$se))
## 
## Random-Effects Model (k = 19; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.1708 (SE = 0.0866)
## tau (square root of estimated tau^2 value):      0.4133
## I^2 (total heterogeneity / total variability):   68.64%
## H^2 (total variability / sampling variability):  3.19
## 
## Test for Heterogeneity:
## Q(df = 18) = 57.3408, p-val < .0001
## 
## Model Results:
## 
## estimate      se    zval    pval   ci.lb   ci.ub 
##   0.6284  0.1176  5.3448  <.0001  0.3980  0.8588  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#plot
GG_forest(main)

GG_save("figs/forest.png")

#precision vs. eff size
GG_scatter(d, "n", "d")
## `geom_smooth()` using formula 'y ~ x'

GG_save("figs/scatter_n_d.png")
## `geom_smooth()` using formula 'y ~ x'
GG_scatter(d, "se", "d")
## `geom_smooth()` using formula 'y ~ x'

GG_save("figs/scatter_se_d.png")
## `geom_smooth()` using formula 'y ~ x'
GG_funnel(main)

GG_save("figs/funnel.png")

#trimfill
trimfill(main)
## 
## Estimated number of missing studies on the left side: 0 (SE = 2.6488)
## 
## Random-Effects Model (k = 19; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.1708 (SE = 0.0866)
## tau (square root of estimated tau^2 value):      0.4133
## I^2 (total heterogeneity / total variability):   68.64%
## H^2 (total variability / sampling variability):  3.19
## 
## Test for Heterogeneity:
## Q(df = 18) = 57.3408, p-val < .0001
## 
## Model Results:
## 
## estimate      se    zval    pval   ci.lb   ci.ub 
##   0.6284  0.1176  5.3448  <.0001  0.3980  0.8588  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
trimfill(main) %>% plot()

#p curve
meta_pcurve(main)

## $p_binomial
## 
##  Exact binomial test
## 
## data:  sum(d$p < 0.025) and nrow(d)
## number of successes = 9, number of trials = 11, p-value = 0.06543
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
##  0.4822441 0.9771688
## sample estimates:
## probability of success 
##              0.8181818 
## 
## 
## $plot